Fisher's Discriminant Function Research Articles

Application of variable selection in the origin discrimination of Wolfiporia cocos (F.A. Wolf) Ryvarden & Gilb. based on near infrared spectroscopy

Dried sclerotium of Wolfiporia cocos (F.A. Wolf) Ryvarden & Gilb. is a traditional Chinese medicine. Its chemical components showed difference among geographical origins, which made it difficult to keep therapeutic potency consistent. The identification of the geographical origin of W. cocos is the fundamental prerequisite for its worldwide recognition and acceptance. Four variable selection methods were employed for near infrared spectroscopy (NIR) variable selection and the characteristic variables were screened for the establishment of Fisher function models in further identification of the origin of W. cocos from Yunnan, China. For the obvious differences between poriae cutis (fu-ling-pi in Chinese, or FLP) and the inner part (bai-fu-ling in Chinese, or BFL) of the sclerotia of W. cocos in the pattern space of principal component analysis (PCA), we established discriminant models for FLP and BFL separately. Through variable selection, the models were significant improved and also the models were simplified by using only a small part of the variables. The characteristic variables were screened (13 for BFL and 10 for FLP) to build Fisher discriminant function models and the validation results showed the models were reliable and effective. Additionally, the characteristic variables were interpreted.

Postharvest assessment of undesirable fibrous tissue (choking hazard) in fresh processing carrots using Vis/NIR hyperspectral images

This research was designed to develop and test an automatic image analysis algorithm to detect the presence (pre-dicing) of undesirable fibrous carrot (Daucus carota L.) tissue. Fibrous carrot dices are difficult to detect, and are highly problematic when found in ready-to-eat infant food, where they might represent a choking hazard (safety concern). A Visible/Near-InfraRed (Vis/NIR) hyperspectral imaging (321-1088 nm) system was used to obtain a set of 520-images per sample, from 1233 sections (samples). Carrots were collected during the 2013 and 2014 harvesting seasons. Classification accuracy per sample was evaluated by comparing the classes obtained using Vis/NIR hyperspectral images against their undesirable fibrous tissue class, based on the industry-simulated invasive quality assessment (% of fiber). Class-0 represents fibrous-free samples, and class-1 denotes samples containing fibrous tissue. After Vis/NIR image preprocessing, cropping, selection, and segmentation, 3135 grayscale intensity and textural features were extracted per sample from 15 selected Vis/NIR images. A 4-fold cross-validation Neural-Network-Classifier with a performance accuracy of 86.4±2.1% was developed using 140 relevant features, which were selected using a sequential forward selection algorithm with the Fisher discriminant objective function. Findings showed that this methodology is an objective, accurate, and reliable tool to determine the presence of undesirable fibrous tissue in processing carrots, and would be applicable to an automated noninvasive inline sorting system.

Postharvest noninvasive classification of tough-fibrous asparagus using computed tomography images

This research was devised to evaluate Computed Tomography (CT) for asparagus fibrousness detection and more specifically develop and test an automatic image analysis method (algorithm) to classify CT images obtained from 859 asparagus (Asparagus officinalis L.) segment (samples), collected during two harvesting seasons (2014 and 2015). Classification accuracy was calculated by comparing the classes obtained using a combination of imaging, image processing, feature extraction, and classification schemes per asparagus segment against an industry-simulated invasive quality assessment.Grayscale intensity and textural features, 3762 total, were extracted from minimum and maximum resultant images from three CT planer views. A 4-fold cross-validation linear discriminant classifier with a performance accuracy of 91.2% was developed using 75 relevant features, which were selected using a sequential forward selection algorithm with the Fisher discriminant objective function. This objective method is accurate in determining the presence of tough-fibrous tissue in asparagus, which demonstrates a potential for such technology to objectively forecast asparagus quality and thus supports the asparagus industry through optimizing consumer acceptability and product utilization.

Postharvest noninvasive assessment of undesirable fibrous tissue in fresh processing carrots using computer tomography images

This research was designed to develop and test an automatic image analysis method (algorithm) to classify CT images obtained from 1233 carrot (Daucus carota L.) sections (samples), collected during the 2013 and 2014 harvesting seasons. Classification accuracy was evaluated by comparing the classes obtained using eighteen CT images per carrot section to their undesirable fibrous tissue class, based on the industry-simulated invasive quality assessment (% of fiber). Class-0 represents fibrous-free samples, and class-1 denotes samples containing fibrous tissue.After CT image preprocessing, cropping, and segmentation, 3762 grayscale intensity and textural features were extracted from the eighteen CT images per sample. A 4-fold cross-validation linear discriminant classifier with a performance accuracy of 87.9% was developed using 95 relevant features, which were selected using a sequential forward selection algorithm with the Fisher discriminant objective function. This objective method is accurate in determining the presence of undesirable fibrous tissue in pre-processed carrots.

Establishment and evaluation of a prognostic model for acute pulmonary embolism

To identify the factors influencing the prognosis of patients with acute pulmonary embolism(PE) and to establish a prognostic model. The clinical data of 331 patients (141 males and 190 females, aged 9 to 87 years ) with acute PE in Fujian Hospital from January 2007 to September 2013 were analyzed. Univariate analysis and logistic regression analysis were used for selecting the independent prognostic factors for acute PE. Based on logistic regression analysis, a prognostic model for PE was established. Univariate analysis showed that statistically significant (all P<0.05) factors influencing the prognosis of PE were diabetes, tricuspid systolic murmur, body temperature, respiratory rate, heart rate, aspartate aminotransferase, triglycerides, abnormal ECG, mechanical ventilation, circulatory failure during hospitalization, risk stratification of PE, types of treatment, and use of low-molecular-weight heparin and Warfarin. Logistic regression analysis showed that recent (<1 month) operation or fracture, tricuspid systolic murmur, high triglyceride level, circulatory failure during hospitalization and mechanical ventilation were independent factors for poor prognosis of PE, while combined use of low-molecular-weight heparin and Warfarin was a protective factor for the prognosis of PE. The Fisher prognostic model equation was y=0.144+ 1.266x1+ 0.869x2+ 1.794x3-0.517x4+ 3.555x5+ 0.661x6. The accuracy of the Fisher discriminant function was 93.0%. Recent (<1 month) operation or fracture, tricuspid systolic murmur, high triglyceride level, shock during hospitalization and mechanical ventilation were signs of poor prognosis for PE, while combined use of low-molecular-weight heparin and Warfarin were beneficial for the prognosis. The discriminant function based on these data can be helpful for predicting the prognosis of patients with PE.

Designing classification filters for integrated sensing and processing using optimal discriminant vectors

Integrated sensing and processing (ISP) is a new strategy for instrument design to simplify quantitative or qualitative analysis. One of the ISP approaches is processing the optical spectrum with filters to obtain analytical results directly. ISP filters based on optimal discriminant vectors are designed in this study for the problem of classification. The method starts with performing principal component analysis (PCA) on the spectra of multi-class samples, and then constructs the optimal orthogonal discriminant vectors using the PCA scores by maximizing Fisher's discriminant function. Therefore, the filters for discriminating the samples can be obtained by transforming the loadings with the discriminant vectors. Applying the filters onto the spectra of new samples, the difference between samples of different class can be obtained and the difference can be used for discrimination of these samples. NIR datasets of vitamins, cephalosporins and Chinese patent medicines are used to test the performance of the filters. The results show that, for each of the datasets, the samples of different class can be correctly identified.

통계 분석을 통한 산사태 토석류 전이규준 모델

최근 들어 집중호우로 인한 산사태 및 토석류 피해가 종종 발생하고 있다. 이에 따라 산사태 재해 예측에 관한 연구 중 산사태 민감도 분석과 토석류 위험도 분석 관련 연구는 활발하게 진행되어 왔지만, 사면 지역에 적용하기 적합한 전이 분석 관련 연구는 부족한 실정이다. 본 연구에서는 판별분석과 로지스틱 회귀 분석과 같은 통계적 방법을 이용하여 실제 토석류가 발생했던 지역에서 추출한 지형학적 인자, 지질학적 인자 등을 토대로 토석류 전이규준을 제시하였다. 10개의 지형학적 및 지질학적 인자가 독립변수로 사용되었으며 실제 466개소(비전이: 228개소, 전이: 238개소)의 토석류 비전이 및 전이 데이터가 수집되었다. 우선, Fisher의 판별 분석이 수행되었으며, 수행 결과 실제경우와 91.6%의 분류 정확도를 보였다. 하지만 전이와 비전이 두 그룹간의 공분산 동질성이 만족되지 않았으며 또한 독립변수들이 정규분포를 보이지도 않았다. 두 번째로 이항 로지스틱 회귀분석이 수행되었으며, 분석 결과 92.3%의 분류 정확도를 나타냈으며 모든 통계적 조건들도 유의하게 나타났다. 따라서 이항 로지스틱 회귀 분석을 이용한 전이 규준은 토석류 재해 발생 여부를 예측하는데 효과적으로 사용될 수 있을 것으로 판단된다. Recently, landslide and debris-flow disasters caused by severe rain storms have frequently occurred. Many researches related to landslide susceptibility analysis and debris-flow hazard analysis have been conducted, but there are not many researches related to mobilization analysis for landslides transforming into debris-flow in slope areas. In this study, statistical analyses such as discriminant analysis and logistic regression analysis were conducted to develop a mobilization criterion using geomorphological and geological factors. Ten parameters of geomorphological and geological factors were used as independent variables, and 466 cases (228 non-mobilization cases and 238 mobilization cases) were investigated for the statistical analyses. First of all, Fisher's discriminant function was used for the mobilization criterion. It showed 91.6 percent in the accuracy of actual mobilization cases, but homogeneity condition of variance and covariance between non-mobilization and mobilization groups was not satisfied, and independent variables did not follow normal distribution, either. Second, binomial logistic analysis was conducted for the mobilization criterion. The result showed 92.3 percent in the accuracy of actual mobilization cases, and all assumptions for the logistic analysis were satisfied. Therefore, it can be concluded that the mobilization criterion for debris-flow using binomial logistic regression analysis can be effectively applied for the prediction of debris-flow hazard analysis.

Detecting credit card fraud by Modified Fisher Discriminant Analysis

In parallel to the increase in the number of credit card transactions, the financial losses due to fraud have also increased. Thus, the popularity of credit card fraud detection has been increased both for academicians and banks. Many supervised learning methods were introduced in credit card fraud literature some of which bears quite complex algorithms. As compared to complex algorithms which somehow over-fit the dataset they are built on, one can expect simpler algorithms may show a more robust performance on a range of datasets. Although, linear discriminant functions are less complex classifiers and can work on high-dimensional problems like credit card fraud detection, they did not receive considerable attention so far. This study investigates a linear discriminant, called Fisher Discriminant Function for the first time in credit card fraud detection problem. On the other hand, in this and some other domains, cost of false negatives is very higher than false positives and is different for each transaction. Thus, it is necessary to develop classification methods which are biased toward the most important instances. To cope for this, a Modified Fisher Discriminant Function is proposed in this study which makes the traditional function more sensitive to the important instances. This way, the profit that can be obtained from a fraud/legitimate classifier is maximized. Experimental results confirm that Modified Fisher Discriminant could eventuate more profit.

Postharvest noninvasive assessment of fresh chestnut (Castanea spp.) internal decay using computer tomography images

X-ray computed tomography (CT) is an effective noninvasive tool to visualize fresh agricultural commodities’ internal components and quality attributes, including those of chestnuts (Castanea spp). There is no procedure to automatically, effectively and efficiently classify fresh commodities from a continuous inline flow through a CT system. If the information obtained by CT scanning of fresh agricultural commodities is to be used in an industrial application (e.g. inline sorting), automated interpretation of CT images is essential. For this purpose, an image analysis method (algorithm) for the automatic classification of CT images obtained from 2848 fresh chestnuts (cv. ‘Colossal’ and ‘Chinese seedlings’), during the harvesting years from 2009 to 2012, was developed and tested. Classification accuracy was evaluated by comparing the classes obtained from six CT images per chestnut to their internal quality assessment. An experienced human rater performed internal quality assessment by visually and invasively rating fresh chestnut internal decay severity (quality) into 5-, 3- and 2-classes.After CT image preprocessing, cropping and segmentation, 1194 grayscale intensity and textural features were extracted from six resultant CT images per sample. Relevant features were selected using a sequential forward selection algorithm with the Fisher discriminant objective function. 86, 155 and 126 features were effective in designing a quadratic discriminant classifier with a 4-fold cross-validation with a performance accuracy of 85.9%, 91.2% and 96.1% for 5, 3 and 2 classes, respectively. This method is accurate and objective in determining fresh chestnut internal quality, and the methodology is applicable to automatic noninvasive inline CT sorting system development.

Learning low-rank and discriminative dictionary for image classification

Dictionary learning plays a crucial role in sparse representation based image classification. In this paper, we propose a novel approach to learn a discriminative dictionary with low-rank regularization on the dictionary. Specifically, we apply Fisher discriminant function to the coding coefficients to make the dictionary more discerning, that is, a small ratio of the within-class scatter to between-class scatter. In practice, noisy information in the training samples will undermine the discriminative ability of the dictionary. Inspired by the recent advances in low-rank matrix recovery theory, we apply low-rank regularization on the dictionary to tackle this problem. The iterative projection method (IPM) and inexact augmented Lagrange multiplier (ALM) algorithm are adopted to solve our objective function. The proposed discriminative dictionary learning with low-rank regularization (D2L2R2) approach is evaluated on four face and digit image datasets in comparison with existing representative dictionary learning and classification algorithms. The experimental results demonstrate the superiority of our approach.

Comparative evaluation of intragastric bile acids and hepatobiliary scintigraphy in the diagnosis of duodenogastric reflux

To assess the diagnostic value of a combination of intragastric bile acids and hepatobiliary scintigraphy in the detection of duodenogastric reflux (DGR). The study contained 99 patients with DGR and 70 healthy volunteers who made up the control group. The diagnosis was based on the combination of several objective arguments: a long history of gastric symptoms (i.e., nausea, epigastric pain, and/or bilious vomiting) poorly responsive to medical treatment, gastroesophageal reflux symptoms unresponsive to proton-pump inhibitors, gastritis on upper gastrointestinal (GI) endoscopy and/or at histology, presence of a bilious gastric lake at > 1 upper GI endoscopy, pathologic 24-h intragastric bile monitoring with the Bilitec device. Gastric juice was aspirated in the GI endoscopy and total bile acid (TBA), total bilirubin (TBIL) and direct bilirubin (DBIL) were tested in the clinical laboratory. Continuous data of gastric juice were compared between each group using the independent-samples Mann-Whitney U-test and their relationship was analysed by Spearman's rank correlation test and Fisher's linear discriminant analysis. Histopathology of DGR patients and 23 patients with chronic atrophic gastritis was compared by clinical pathologists. Using the Independent-samples Mann-Whitney U-test, DGR index (DGRi) was calculated in 28 patients of DGR group and 19 persons of control group who were subjected to hepatobiliary scintigraphy. Receiver operating characteristic curve was made to determine the sensitivity and specificity of these two methods in the diagnosis of DGR. The group of patients with DGR showed a statistically higher prevalence of epigastric pain in comparison with control group. There was no significant difference between the histology of gastric mucosa with atrophic gastritis and duodenogastric reflux. The bile acid levels of DGR patients were significantly higher than the control values (Z: TBA: -8.916, DBIL: -3.914, TBIL: -6.197, all P < 0.001). Two of three in the DGR group have a significantly associated with each other (r: TBA/DBIL: 0.362, TBA/TBIL: 0.470, DBIL/TBIL: 0.737, all P < 0.001). The Fisher's discriminant function is followed: Con: Y = 0.002TBA + 0.048DBIL + 0.032TBIL - 0.986; Reflux: Y = 0.012TBA + 0.076DBIL + 0.089TBIL - 2.614. Eighty-four point zero five percent of original grouped cases were correctly classified by this method. With respect to the DGR group, DGRi were higher than those in the control group with statistically significant differences (Z = -5.224, P < 0.001). Twenty eight patients (59.6%) were deemed to be duodenogastric reflux positive by endoscopy, as compared to 37 patients (78.7%) by hepatobiliary scintigraphy. The integrated use of intragastric bile acid examination and scintigraphy can greatly improve the sensitivity and specificity of the diagnosis of DGR.

EXPERIMENTAL STUDY ON QUALITY INTIMA-MEDIA THICKNESS AND QUALITY ARTERIAL STIFFNESS IN EARLY ATHEROSCLEROSIS IN RABBITS

ObjectivesTo evaluate the characteristics for the changes and the feasibility of Quality Intima-media Thickness (QIMT) and Quality Arterial Stiffness (QAS) in different pathological grades of atherosclerosis by using pathological indexes...

Determination and pattern recognition of trace elements in serum samples from patients with renal cell carcinoma by ICP-MS

Objective To study the relationship between serum trace elements and renal cell carcinoma.Methods The serum concentrations of multi-elements in 34 patients with renal cell carcinoma and 28 healthy volunteers were determined by inductively coupled plasma mass spectrometry(ICP-MS).The results were analyzed by partial least square discriminant analysis (PLS-DA) and Fisher discriminant.Results Compared with healthy voluteers,the levels of vanadium (5 034.56 ng/L:4 401.23 ng/L ),cobalt (211.34 ng/L:158.67 ng/L),nickel(l 850.55 ng/L:1 587.12 ng/L),manganese(1 873.35 ng/L:932.68 ng/L) and cadmium(95.63 ng/L:36.43 ng/L) were significantly higher in patients with renal cell carcinoma (P ＜ 0.05 ).While,the concentrations of calcium( 10.83 mg/L:11.78 mg/L) and zinc(67.11 μg/L:70.92 pg/L)were significantly lower ( P ＜ 0.05 ).Discriminant analysis showed that the serum elements levels in the patients with renal cell carcinoma were significantly different from the healthy volunteers.The scores plot showed distinct clustering between patients and controls,the points of patients were obviously offset from the controls.The classification accuracy of Fisher discriminant function was 97.61％.Conclusion Trace elements in serum are significantly different in patients with renal cell carcinoma and healthy volunteers.Discriminant analysis of serum samples based on trace element levels is possible.Thus,it is feasible for early diagnosis of renal cell carcinoma by determination of trace elements and discriminant analysis. Key words: Kidney neoplasms; Mass spectrometry; Trace elements; Discriminant analysis

Prediction of Water Inrush from Coal Floor Based on Fisher Discriminant Analysis

Based on the theory of Fisher discriminant analysis, this paper aims to develop the Fisher discriminant function model by using the representative water inrush data from the floor of coal mining face, considering comprehensively the factors that affect the water inrush from coal floor. The predicted result is consistent with the actual situation, which corresponds with or better than that of the least squares support vector machine, artificial neural network, and so on. The results indicate that this model is a new effective way of predicting the water inrush from floor and can be widely used in the practical engineering because of the low resubstitution error rate and good distinguishing performance.

From centralized to distributed decision tree induction using CHAID and fisher's linear discriminant function algorithms

The decision tree-based classification is a popular approach for pattern recognition and data mining. Most decision tree induction methods assume training data being present at one central location. Given the growth in distributed databases at geographically dispersed locations, the methods for decision tree induction in distributed settings are gaining importance. This paper extends two well-known decision tree methods for centralized data to distributed data settings. The first method is an extension of CHAID algorithm and generates single feature based multi-way split decision trees. The second method is based on Fisher's linear discriminant (FLO) function and generates multifeature binary trees. Both methods aim to generate compact trees and are able to handle multiple classes. The suggested extensions for distributed environment are compared to their centralized counterparts and also to each other. Theoretical analysis and experimental tests demonstrate the effectiveness of the extensions. In addition, the side-by-side comparison highlights the advantages and deficiencies of these methods under different settings of the distribution environments.

Kernel Local Fuzzy Clustering Margin Fisher Discriminant Method Faced on Fault Diagnosis

In order to better identify the fault of rotor system,one new method based on local fuzzy clustering margin fisher discriminant (LFCMFD) was proposed. For each point on manifold, the farthest point in local neighborhood and the nearest point outside local neighborhood usually constituted the local margin. LFCMFD introduced fuzzy cluster analysis algorithm, eliminated the influence of pseudo-margin points, obtained real local margin, compute with-class scatter and between-class scatte, established local magin fisher discriminant function, found optimal fault diagnosis vector,and then identified the fault class of new testing data by this vector. In order to improve the nonlinear analysis ability of LFCMFD, considering kernel mapping idea, training data with supervision information were mapped to kernel space, constructed kernel fisher discriminant function, LFCMFD algorithm based on kernel method (KLFCMFD)was proposed. The experiment showed, KLFCMFD algorithm had best effect in comparison to other manifold learning algorithm to the rotor fault diagnosis,and fully identify fault class when selecting the appropriate parameters.

KLASIFIKASI OBJEK DALAM VISI KOMPUTER DENGAN ANALISIS DISKRIMINAN

A robotic sensor system is always supported by a computer system called ‘computer vision’. The important concept of computer vision is object classfifi cation. In this study two algorithms for object classifi cation in this system will be compared. Firstly, A simple method that do not need complex computation and that considered as an informal method is called binary tree decision structure. This method is based on modest caracteristic decriptors of an object such as vertical line, horizontal line or ellipse line. Unfortunately this method has weakness in recognize an image that contaminated by a noise. Secondly, a more formal method with high variability descriptors. In this contect a multivariate statistical approach named discriminant analysis is proposed as an alternative for object classifi cation. This method is operated by computation of a function called Fisher discriminant function that can be used for separating an object. From the data simulation and analysis for calssifi cation of two object i.e. screw and bolt and three objects i.e. alphabet T,O and S it can be shown that discriminant analysis approach can classify an object better than binary decision algorithm. The superority of discriminant method is especially seen when this method is applied for classifi cation of a noisy image of object.

Cluster-fusion recognition method for rivet lines based on Fisher discriminant criterion function

A cluster-fusion recognition method for rivet lines was proposed based on Fisher discriminant criterion function.The edge points were extracted using Canny operator,and processed by morphology in order to get the location of the centroid.Then,any two centroids were combined to construct the collection of line segments which were classified by Fisher criterion.According to the principle that the distance from the segments on one rivet line to origin point is similar,the segment-vectors were fused to fit the rivet line.The experimental results show that its accuracy and robustness are improved,which can meet the requirements of real-time detection.

DALASS: Variable selection in discriminant analysis via the LASSO

The objective of DALASS is to simplify the interpretation of Fisher's discriminant function coefficients. The DALASS problem—discriminant analysis (DA) modified so that the canonical variates satisfy the LASSO constraint—is formulated as a dynamical system on the unit sphere. Both standard and orthogonal canonical variates are considered. The globally convergent continuous-time algorithms are illustrated numerically and applied to some well-known data sets.

Universal non-data-aided small-sample-support adaptive spread-spectrum receivers

We revisit the problem of recovering a spread-spectrum (SS) signal in the presence of unknown correlated SS interference and additive Gaussian or impulsive noise. The general receiver structure is composed of an adaptive chip-based nonlinear pre-processor followed by an adaptive linear filter. We propose two blind algorithms for the adaptation of the parameters of the nonlinear pre-processor that are based on the Fisher discriminant function evaluated at the linear filter output. The first algorithm couples itself with the adaptation of the linear post-processor, while the second algorithm is a decoupled procedure that is independent of the linear post-processor. Both algorithms lead to universal receiver solutions in the sense that they mitigate a wide range of additive noise effects (from purely Gaussian to impulsive) and correlated SS interference. Both algorithms offer superior detection performance when receiver adaptation is carried out with limited input data observations